Cataloging or monitoring surface topology, like on the Earth's surface, can have a variety of applications. This is often referred to as “terrain mapping.” A terrain map may be represented in a number of ways and include digital elevation models (DEM), digital terrain models (DTM) and digital surface models (DSM). A terrain map may also be represented by a land class database. Land class databases can fee similar to a DEM in terms of the size of the “pixels” (e.g., each “pixel” could measure several by several meters) however the content of this database is different. For example, one exemplary land class database comprises 13 different land classes. Some of the land classes used in certain embodiments of the disclosure include, grass, wetland, snow, desert, forest, road, urban, agriculture, barren, shrub, water, and rice.
Terrain mapping is useful in health and fitness applications, where the time and distance spent on different terrains may be recorded for use by users and/or health professionals. Due to the dynamic nature of the Earth's terrain a “real-time” terrain map based, in part, on gait analysis would also be advantageous. Terrain mapping may also be useful in location mapping and as a compliment to existing forms of personal navigation.
GPS is a robust and accurate technique for determining location, when a user's receiver has un-occluded reception from multiple satellites; however, in many cases GPS signals are denied. Some examples of GPS-denied areas include indoors, parking garages, tunnels, areas where GPS is occluded by vegetation, zones with skyscrapers, areas when there is a multi-path, and the like. Additionally, there are some areas on earth where adversaries intentionally deny GPS signals by spoofing and jamming. Therefore, alternative to GPS navigation techniques are currently being developed, such as Vision-Aided Navigation. Being able to use a method such as gait analysis for terrain mapping may enable a more complete assessment of a user's location, particularly in GPS-denied areas. This could be beneficial in a number of applications, especially for dismounted infantry, who often operate in mixed outdoor/indoor environments.
Computer vision based approaches to gait analysis extract motion features from image sequences. These approaches are, in general, susceptible to variations in viewing geometry, background clutter, varying appearances, uncontrolled lighting conditions, and low image resolutions. Similarly, measurements from floor pressure sensors have been explored for gait recognition, but cameras or pressure sensors are part of infrastructure, while inertial sensors, as described herein, are worn by an individual. Because no one can install infrastructure over large areas, especially on enemy's territory, a method of personal navigation that does not rely on external infrastructure is needed.
In the past decade, accelerometers have been intensely researched for gait and activity analysis. Such sensors are advantageous compared to both videos and floor sensors for gait analysis. As described above, vision based approaches inter body motion from clattered images. That is highly ambiguous, error prone, and vulnerable to variations in a number of external factors. In contrast, inertial sensors directly measure human body motion to achieve more accurate gait analysis. Inertial sensors are also inexpensive, small in size, and very easy to deploy. Mobile devices such as smart phones and tablets use accelerometers, gyroscopes, and the like to automatically determine the screen layout for improved user experience. In one embodiment of the disclosure, the ubiquity of mobile devices embedded with inertial sensors is used to collect motion data continuously for unobtrusive gait-based authentication and identification.
Typical triple axis accelerometers capture accelerations along three orthogonal axes of the sensor. Given a multivariate time series of the acceleration data, feature vectors are usually extracted for signal windows corresponding to each detected gait cycle or for windows of a pre-specified size. These windows are compared and matched based on template matching, using either the correlation method or dynamic time warping. Alternatively, statistical features including mean, standard deviations, or time span between peaks in windows, histograms, entropy, higher order moments, and features in spatial domain are also used. Fast Fourier Transforms (FFT) and wavelet coefficients in frequency domain are used to compare longer sequences. Classifiers including nearest neighbor classifier, support vector machine (SVM), and Kohonen self-organizing maps have been used. In some cases, preprocessing such as a weighted moving average is applied to suppress the noise in data.
Currently, accelerometers and other inertial sensors only measure local motion where they are worn, and motion patterns differ from one part of the body to another due to the articulated nature of body motion. Even when the sensor is placed at a fixed location, the data measurements can still change depending on the orientation of the sensors. Most existing research has been conducted in controlled laboratory settings to minimize these variations. In some cases, the sensors are placed in a specific way so that intuitive meanings can be assigned to the data components and exploited for gait analysis. As such, existing methods are susceptible to errors when used in real-world conditions. Mobile devices are usually carried in pockets or hands without constraints in orientation. Since the same external motion results in completely different measurements with changing sensor orientation, it is essential to compute an individual's gait independent of sensor rotation for realistic scenarios.
For a mobile device based gait analysis system to succeed in real world applications, such as terrain mapping and personal navigation, it is crucial to address the variations in sensor orientation due to casual handling of mobile devices. It is also crucial to address variation in pace and terrain to accurately use gait analysis in such applications.